To address this need, there has been increasing interest in adopting pretraining weights, which can be transferred to downstream medical tasks with limited labeled data. A common method transfers model weights from supervised ImageNet-pretraining [1]}, [2]}, yet there is a considerable gap between the natural image and medical image domains. In response to questions raised about the method's effectiveness in medical contexts [3]}, some have attempted to bridge the domain gap by pretraining on existing large-scale labeled medical datasets [4]}. However, even this type of supervised, in-domain pretraining mostly relies on semantically sparse learning signals; labels are extracted through natural language processing (NLP) on accompanying text reports [5]}, [6]}, producing large-scale yet noisy annotations [7]} not ideal for building efficient representations for transfer learning.
